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With the increasing demand for Chinese cold chain logistics, accurate forecasting becomes significantly important for decision-making and planning, but the data scarcity is still a challenging problem for modeling method. This paper proposes a novel grey prediction model with bi-level structure to improve the performance of cold chain logistics demand forecasting. The model adapts to the data characteristics and generates corresponding background series to adjust the parameters. A differential evolutionary algorithm is employed to merge the time response functions generated from the bi-level structure. The proposed model extends the applicability compared with existing similar models and can provide higher accuracy for data sequences with a high developing rate. The model outperforms the classic grey model and its extensions in terms of forecasting accuracy. The trend of Chinese cold chain logistics demand from 2021 to 2025 is studied with the proposed model. The computational results show that the proposed grey prediction model is significantly better than other models. [ABSTRACT FROM AUTHOR]